Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning
Abstract Background Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LTA detection in wearable monitoring systems is of great importance. One of the main challenges in building robust LTA detection algorithms is the limited availability of labeled LTA data. Metho...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00982-9 |
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| author | Giuliana Monachino Beatrice Zanchi Michael Wand Giulio Conte Athina Tzovara Francesca Dalia Faraci |
| author_facet | Giuliana Monachino Beatrice Zanchi Michael Wand Giulio Conte Athina Tzovara Francesca Dalia Faraci |
| author_sort | Giuliana Monachino |
| collection | DOAJ |
| description | Abstract Background Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LTA detection in wearable monitoring systems is of great importance. One of the main challenges in building robust LTA detection algorithms is the limited availability of labeled LTA data. Methods We introduce an effective deep-learning algorithm for detecting LTAs from single-lead ECGs in out-of-hospital cardiac arrest applications. We address the data-scarcity issue by applying a transfer learning approach. The deep-learning model is pre-trained on a massive dataset (72’952 recordings) for rhythm classification and then fine-tuned on the target dataset with LTA events (102 recordings). Results Our model achieves a sensitivity of 92.68% and a specificity of 99.48%, with a granularity of 1.28 seconds, in detecting LTAs. Additionally, a confidence estimation procedure is introduced to enable emergency service pre-alerts in case of low-confidence detections. Conclusions Our transfer learning based approach has the potential to significantly mitigate the impact of data scarcity, advancing LTA detection in wearable monitoring systems, and supporting rapid, life-saving interventions in out-of-hospital cardiac arrest emergencies. |
| format | Article |
| id | doaj-art-49e7e85b85714aa6a88a543c2ab2da6c |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-49e7e85b85714aa6a88a543c2ab2da6c2025-08-20T04:01:36ZengNature PortfolioCommunications Medicine2730-664X2025-07-015111010.1038/s43856-025-00982-9Overcoming data scarcity in life-threatening arrhythmia detection through transfer learningGiuliana Monachino0Beatrice Zanchi1Michael Wand2Giulio Conte3Athina Tzovara4Francesca Dalia Faraci5Institute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern SwitzerlandInstitute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern SwitzerlandInstitute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern SwitzerlandCardiology Department, Cardiocentro Ticino Institute, Ente Ospedaliero CantonaleInstitute of Informatics, University of BernInstitute of Digital Technologies for Personalized Healthcare - MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern SwitzerlandAbstract Background Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LTA detection in wearable monitoring systems is of great importance. One of the main challenges in building robust LTA detection algorithms is the limited availability of labeled LTA data. Methods We introduce an effective deep-learning algorithm for detecting LTAs from single-lead ECGs in out-of-hospital cardiac arrest applications. We address the data-scarcity issue by applying a transfer learning approach. The deep-learning model is pre-trained on a massive dataset (72’952 recordings) for rhythm classification and then fine-tuned on the target dataset with LTA events (102 recordings). Results Our model achieves a sensitivity of 92.68% and a specificity of 99.48%, with a granularity of 1.28 seconds, in detecting LTAs. Additionally, a confidence estimation procedure is introduced to enable emergency service pre-alerts in case of low-confidence detections. Conclusions Our transfer learning based approach has the potential to significantly mitigate the impact of data scarcity, advancing LTA detection in wearable monitoring systems, and supporting rapid, life-saving interventions in out-of-hospital cardiac arrest emergencies.https://doi.org/10.1038/s43856-025-00982-9 |
| spellingShingle | Giuliana Monachino Beatrice Zanchi Michael Wand Giulio Conte Athina Tzovara Francesca Dalia Faraci Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning Communications Medicine |
| title | Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning |
| title_full | Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning |
| title_fullStr | Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning |
| title_full_unstemmed | Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning |
| title_short | Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning |
| title_sort | overcoming data scarcity in life threatening arrhythmia detection through transfer learning |
| url | https://doi.org/10.1038/s43856-025-00982-9 |
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